Connectivity concepts in neuronal network modeling (original) (raw)
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Efficient generation of connectivity in neuronal networks from simulator-independent descriptions
Frontiers in neuroinformatics, 2014
Simulator-independent descriptions of connectivity in neuronal networks promise greater ease of model sharing, improved reproducibility of simulation results, and reduced programming effort for computational neuroscientists. However, until now, enabling the use of such descriptions in a given simulator in a computationally efficient way has entailed considerable work for simulator developers, which must be repeated for each new connectivity-generating library that is developed. We have developed a generic connection generator interface that provides a standard way to connect a connectivity-generating library to a simulator, such that one library can easily be replaced by another, according to the modeler's needs. We have used the connection generator interface to connect C++ and Python implementations of the previously described connection-set algebra to the NEST simulator. We also demonstrate how the simulator-independent modeling framework PyNN can transparently take advantage...
Model descriptions in Neuroscience: computational performance and collaboration
HAL (Le Centre pour la Communication Scientifique Directe), 2022
This report reviews the state-of-the-art of model descriptions for largescale neuronal network models in computational neuroscience, with a particular focus on issues of collaborative model development and of performance on large clusters and supercomputers. After summarising the requirements for this class of models, and the capabilities of existing simulation tools and existing model description formats, we analyse the shortcomings of existing tools and languages, and make recommendations for future development in this domain; in particular: the use of mixed, standardised text and binary file formats (e.g. YAML/JSON with parallel HDF5); alternative/interoperable representations to support a wide range of use cases from algorithm-driven point-neuron networks to data-driven biophysically-detailed networks; and the development of conversion tools to allow gradual convergence without disruption to ongoing projects.
PLOS Computational Biology, 2010
Biologically detailed single neuron and network models are important for understanding how ion channels, synapses and anatomical connectivity underlie the complex electrical behavior of the brain. While neuronal simulators such as NEURON, GENESIS, MOOSE, NEST, and PSICS facilitate the development of these data-driven neuronal models, the specialized languages they employ are generally not interoperable, limiting model accessibility and preventing reuse of model components and cross-simulator validation. To overcome these problems we have used an Open Source software approach to develop NeuroML, a neuronal model description language based on XML (Extensible Markup Language). This enables these detailed models and their components to be defined in a standalone form, allowing them to be used across multiple simulators and archived in a standardized format. Here we describe the structure of NeuroML and demonstrate its scope by converting into NeuroML models of a number of different voltage-and ligand-gated conductances, models of electrical coupling, synaptic transmission and short-term plasticity, together with morphologically detailed models of individual neurons. We have also used these NeuroML-based components to develop an highly detailed cortical network model. NeuroML-based model descriptions were validated by demonstrating similar model behavior across five independently developed simulators. Although our results confirm that simulations run on different simulators converge, they reveal limits to model interoperability, by showing that for some models convergence only occurs at high levels of spatial and temporal discretisation, when the computational overhead is high. Our development of NeuroML as a common description language for biophysically detailed neuronal and network models enables interoperability across multiple simulation environments, thereby improving model transparency, accessibility and reuse in computational neuroscience.
Synaptic Connectivity in Anatomically Realistic Neural Networks: Modeling and Visual Analysis
2012
The structural organization of neural circuitry is an important determinant of brain function. Thus, knowing the brain’s wiring (the connectome) is key to understanding how it works. For example, understanding how sensory information is translated into behavior requires a comprehensive view of the microcircuits performing this translation at the level of individual neurons and synapses. Obtaining a wiring diagram, however, is nontrivial due to size, complexity and accessibility of the involved brain regions. Even when such data were available, it were difficult to analyze. Here we describe how a network of ∼0.5 million neurons and their synaptic connections, representing the vibrissal area of the rat primary somatosensory cortex, can be reconstructed. Furthermore, we present a framework for visual exploration of synaptic connectivity between (groups of) neurons within this model. It includes, first, the Cortical Column Connectivity Viewer (CCCV) that provides a hybrid abstract/spati...
Realistic modeling of neurons and networks: towards brain simulation
Functional neurology, 2013
Realistic modeling is a new advanced methodology for investigating brain functions. Realistic modeling is based on a detailed biophysical description of neurons and synapses, which can be integrated into microcircuits. The latter can, in turn, be further integrated to form large-scale brain networks and eventually to reconstruct complex brain systems. Here we provide a review of the realistic simulation strategy and use the cerebellar network as an example. This network has been carefully investigated at molecular and cellular level and has been the object of intense theoretical investigation. The cerebellum is thought to lie at the core of the forward controller operations of the brain and to implement timing and sensory prediction functions. The cerebellum is well described and provides a challenging field in which one of the most advanced realistic microcircuit models has been generated. We illustrate how these models can be elaborated and embedded into robotic control systems to gain insight into how the cellular properties of cerebellar neurons emerge in integrated behaviors. Realistic network modeling opens up new perspectives for the investigation of brain pathologies and for the neurorobotic field.
Biologically Realistic Modelling of Cortical Network Dynamics
Contents 1 Introduction 3 1.1 Thesis outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2 Introduction to Neuroscience 5 2.1 The neuron . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.1.1 The membrane potential . . . . . . . . . . . . . . . . . . . 5 2.2 Cerebral cortex . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 3 The Hodgkin-Huxley Equations 11 3.1 The squid membrane . . . . . . . . . . . . . . . . . . . . . . . . . 11 3.2 The mathematical model . . . . . . . . . . . . . . . . . . . . . . . 12 3.2.1 The potassium conductance . . . . . . . . . . . . . . . . . 13 3.2.2 The sodium conductance . . . . . . . . . . . . . . . . . . 15 3.3 The dynamics of a Hodgkin-Huxley neuron . . . . . . . . . . . . 16 4 The Model 21 4.1 The neurons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 4.2 The network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23<F14.26
RateML: A Code Generation Tool for Brain Network Models
Frontiers in Network Physiology, 2022
Whole brain network models are now an established tool in scientific and clinical research, however their use in a larger workflow still adds significant informatics complexity. We propose a tool, RateML, that enables users to generate such models from a succinct declarative description, in which the mathematics of the model are described without specifying how their simulation should be implemented. RateML builds on NeuroML’s Low Entropy Model Specification (LEMS), an XML based language for specifying models of dynamical systems, allowing descriptions of neural mass and discretized neural field models, as implemented by the Virtual Brain (TVB) simulator: the end user describes their model’s mathematics once and generates and runs code for different languages, targeting both CPUs for fast single simulations and GPUs for parallel ensemble simulations. High performance parallel simulations are crucial for tuning many parameters of a model to empirical data such as functional magnetic ...
Neuromorphic modeling abstractions and simulation of large-scale cortical networks
2011 IEEE/ACM International Conference on Computer-Aided Design (ICCAD), 2011
Biological neural systems are well known for their robust and power-efficient operation in highly noisy environments. We outline key modeling abstractions for the brain and focus on spiking neural network models. We discuss aspects of neuronal processing and computational issues related to modeling these processes. Although many of these algorithms can be efficiently realized in specialized hardware, we present a case study of simulation of the visual cortex using a GPU based simulation environment that is readily usable by neuroscientists and computer scientists and efficient enough to construct very large networks comparable to brain networks.
PyNN: a common interface for neuronal network simulators
Frontiers in …, 2008
Computational neuroscience has produced a diversity of software for simulations of networks of spiking neurons, with both negative and positive consequences. On the one hand, each simulator uses its own programming or configuration language, leading to considerable difficulty in porting models from one simulator to another. This impedes communication between investigators and makes it harder to reproduce and build on the work of others. On the other hand, simulation results can be cross-checked between different simulators, giving greater confidence in their correctness, and each simulator has different optimizations, so the most appropriate simulator can be chosen for a given modelling task. A common programming interface to multiple simulators would reduce or eliminate the problems of simulator diversity while retaining the benefits. PyNN is such an interface, making it possible to write a simulation script once, using the Python programming language, and run it without modification on any supported simulator (currently NEURON, NEST, PCSIM, Brian and the Heidelberg VLSI neuromorphic hardware). PyNN increases the productivity of neuronal network modelling by providing high-level abstraction, by promoting code sharing and reuse, and by providing a foundation for simulator-agnostic analysis, visualization and data-management tools. PyNN increases the reliability of modelling studies by making it much easier to check results on multiple simulators. PyNN is open-source software and is available from http://neuralensemble.org/PyNN.